@Marvin I have many tasks. If one fails, I want it to report as failed, but continue the other tasks.

To handle task failures in Prefect while allowing other tasks to continue executing: 1. Task Retries: Each task in Prefect can be configured with retries, meaning that if a task fails, it can automatically be retried a specified number of times with an optional delay between retries. You can add retry logic to your task definitions which may help in transient error scenarios. 2. Graceful Failure: Some tasks might experience permanent errors. For such cases, you can define fallback behaviors using

try/except

blocks in your tasks or using a state handler to catch exceptions and return a

Failed

state without raising the exception, allowing other tasks to proceed. 3. Notifications: You might want to be notified when a task fails. This can be achieved by setting notifications using Prefect's notification system to alert your team or log the issue for later investigation. For detailed implementation, you should consult the Prefect documentation or guide on task retries, state handling, and notifications to configure your workflow according to your requirements. Remember, you also have the option to set

skip_on_upstream_skip

to

True

on your tasks, which will tell Prefect not to run the task if upstream tasks have failed and been skipped. Implementing the above will ensure that while one task reports as failed, the other tasks in your flow can continue execution as per your designed workflow.

@Marvin can you show me code example on how to do this: using a state handler to catch exceptions and return a

Failed

state without raising the exception, allowing other tasks to proceed.

Certainly. Below is a Python code example of using a state handler in Prefect to catch exceptions, return a

Failed

state for the task, and allow the other tasks to continue their execution:

python
from prefect import task, Flow
from prefect.engine.state import Failed

# Define a custom state handler
def custom_state_handler(task, old_state, new_state):
    if new_state.is_failed():
        # Do something when the task fails, like logging
        print(f"Task {task.name} failed: {new_state.message}")
        # Return a Failed state without raising an exception
        return Failed(message="Task failed gracefully", result=new_state.result)
    return new_state

# Define a task with the custom state handler
@task(state_handlers=[custom_state_handler])
def fragile_task():
    raise ValueError("Something went wrong")

# Define another task that will proceed even if `fragile_task` fails
@task
def resilient_task():
    return "I am unstoppable"

# Create a flow and add tasks
with Flow("My Flow") as flow:
    task_1 = fragile_task()
    task_2 = resilient_task()

# Run the flow
flow_state = flow.run()

In this example, the

fragile_task

is designed to fail (it raises a

ValueError

). The

custom_state_handler

function is a state handler that checks if the new state of the task is failed. It prints a log message and returns a

Failed

state without raising an exception. When you run this flow, the

fragile_task

will fail, but the

resilient_task

will execute successfully, demonstrating that a task failing does not prevent other tasks from running. Keep in mind that this is a simplified example for demonstration purposes. In real-world scenarios, you would likely want to implement more comprehensive error handling and logging within your state handler.

here's an example of returning manual state. you could do something similar in a try/except block where you're handling the exception and returning a failed state

@Marvin Does Prefect UI allow me to go to a task that failed, and run it again. (concept of a repair run)

According to the document excerpts from Prefect's blog, the Prefect UI allows you to see the flow runs that have failed and also supports rerunning flows. However, the excerpts don't specifically mention the rerunning of individual tasks (also known as a "repair run") from the UI. In general, to rebalance tasks that have failed, you would re-run the flow, potentially with updated parameters or code to address the reason for the failure. For sophisticated use cases, you may also investigate using Prefect's API or the CLI to trigger runs, which could give you more fine-grained control over which tasks to execute. For the most accurate and up-to-date information, it's always best to refer to the official Prefect documentation or reach out to the Prefect community for support on specific features like repair runs for individual tasks.

@Marvin If i re-run the flow, then wouldn't the things which already completed run again for no reason?

In many workflow systems, including Prefect, re-running a flow generally means executing the entire workflow from the beginning, which would indeed cause tasks that have already completed successfully to run again. To avoid this redundancy and inefficiency, Prefect 2.x (Orion) introduced more granular re-run capabilities. Prefect's concept of late binding allows the workflow to determine task execution based on the state of upstream tasks at runtime. Thus, with specific configurations, you can perform what is known as an idempotent run, where only the tasks that failed—or their downstream dependent tasks—are rerun. Additionally, Prefect may offer features like caching or checkpointing, which can be used to prevent re-execution of certain tasks that do not need to be rerun. With these features, the result of a task can be stored and reused if the task is being rerun within a certain timeframe, thus preventing unnecessary computation. For concrete details on how to implement such functionality, you should consult the Prefect documentation, specifically looking for terms like "idempotent runs," "caching," "result handlers," "checkpoints," and "state-dependent reruns." If you're using Prefect Cloud or the UI, there might be ways to trigger runs of specific tasks within a flow, but this behavior can vary depending on the version and the specific setup of your Prefect installation.

not if you're persisting results. then we can skip successful tasks

thanks for your help kevin! I've got to run, will continue my exploration later.

Here's how you could wrap your Prefect task in a

try/except

block to catch exceptions, suppress them, and mark the task as failed:

python
from prefect import task, Flow
from prefect.engine.signals import FAIL

@task
def potentially_failing_task():
    try:
        # Your task logic here
        raise ValueError("Something went wrong")
        # If the task succeeds, you can return a result
        return "Task completed successfully"
    except Exception as e:
        # If there's an error, catch it and don't re-raise it
        # Use Prefect's signals to change the task state to `Failed`
        FAIL(f"Task failed due to error: {e}")

# Define your flow and add this task
with Flow("My Flow") as flow:
    result = potentially_failing_task()

# Run the flow
flow_state = flow.run()

In this example, the task

potentially_failing_task

is where you implement the business logic that might fail. If an error occurs, the exception is caught, and the Prefect signal

FAIL

is used to indicate a task failure. This signal changes the task state to

Failed

, allowing other tasks in the flow to continue running if they are not dependent on the outcome of the failed task. Note that the use of Prefect's

FAIL

signal (instead of just returning

Failed

) helps inform the Prefect engine of the error, allowing it to handle the task state appropriately, rather than just suppressing the exception silently. This is an important distinction because it allows Prefect to properly manage task states and dependencies.

@Marvin illustrate the above with 3 tasks, A, B, C. B is dependent on A successfully completing. C should always run. All errors should be captured, set the state to Failure. And C should always run, even if A or B fails.

Here's how you can define three tasks in Prefect with the specified dependencies and failure handling:

python
from prefect import task, Flow
from prefect.engine.signals import FAIL

@task
def task_A():
    try:
        # Task A logic
        # For demonstration, let's say it fails:
        raise ValueError("Task A encountered an error")
    except Exception as e:
        # Use Prefect's signals to set state to `Failed`
        FAIL(f"Task A failed: {e}")

@task
def task_B(upstream_result):
    try:
        # Task B logic, which depends on the result of task A
        # If task A failed, this block is skipped
        # For demonstration, let's say task B succeeds:
        return "Task B completed successfully"
    except Exception as e:
        # Use Prefect's signals to set state to `Failed`
        FAIL(f"Task B failed: {e}")

@task(trigger=prefect.triggers.always_run)
def task_C():
    try:
        # Task C logic, which runs regardless of upstream tasks
        return "Task C completed successfully"
    except Exception as e:
        # Use Prefect's signals to set state to `Failed`
        FAIL(f"Task C failed: {e}")

# Define the flow
with Flow("My Flow") as flow:
    result_A = task_A()
    result_B = task_B(upstream_result=result_A)
    result_C = task_C()

# Set task dependencies
flow.set_dependencies(task_B, upstream_tasks=[task_A])
flow.set_dependencies(task_C, upstream_tasks=[], downstream_tasks=[task_A, task_B])

# Run the flow
flow_state = flow.run()

In this example: - Task A is a standalone task that can fail. - Task B depends on Task A's success to execute. If Task A raises an exception and its state is set to

FAIL

, Task B will be skipped by default, unless you configure Task B with an appropriate trigger. - Task C should always run, regardless of the success or failure of Task A and B. This is achieved by setting

trigger=prefect.triggers.always_run

. Keep in mind that

FAIL

will stop the task execution and mark the task as failed, but it won't raise an exception that would stop the entire flow. Task B has an

upstream_result

parameter, which is a placeholder for Task A’s result. In practice, you would process

upstream_result

within Task B, but if Task A fails, Task B will not run unless you set a trigger that tells Prefect to run B no matter what. Would you like to fine-tune this example or explore other Prefect features?

@Marvin is this prefect V1 or V2? I thought V2 uses wait_for

My apologies for any confusion. The example given mixes elements from Prefect 1.0 'Core' and Prefect 2.0 'Orion'. The concept of using

FAIL

is more aligned with Prefect Core, and in Prefect Orion, tasks would handle failures differently. In Prefect 2.0 'Orion', we don't use the

FAIL

signal. Instead, we use

raise

to indicate task failure. And to make sure a task always runs, regardless of upstream tasks, Prefect Orion has the concept of specifying

wait_for

directly in the function decorator to define task dependencies. Here is an example of three tasks in Prefect 2.0 'Orion', with the specified behavior:

python
from prefect import flow, task
from prefect.engine import get_run_logger

@task
def task_A():
    try:
        # Task A logic
        raise ValueError("Task A encountered an error")
    except Exception as e:
        logger = get_run_logger()
        logger.error(f"Task A failed: {e}")
        raise e

@task
def task_B():
    try:
        # Task B logic
        logger = get_run_logger()
        <http://logger.info|logger.info>("Task B completed successfully")
    except Exception as e:
        logger = get_run_logger()
        logger.error(f"Task B failed: {e}")
        raise e

@task
def task_C():
    try:
        # Task C logic
        logger = get_run_logger()
        <http://logger.info|logger.info>("Task C completed successfully")
    except Exception as e:
        logger = get_run_logger()
        logger.error(f"Task C failed: {e}")
        raise e

@flow
def flow_ABC():
    # Start task A and wait for it to finish
    task_A_result = task_A()
    
    # Start task B if task A succeeded
    if task_A_result.is_successful():
        task_B()

    # Task C will always run, regardless of A or B
    task_C()

flow_ABC()

In this example: - Task B will only run if Task A is successful. - Task C will always run since it's not placed in any conditional logic dependent on the state of Task A or B. Additionally, please note that this is a simplified example. In practical use cases, you will want proper error handling and possibly use other features of Prefect Orion to manage task states and dependencies more effectively. Be sure to always check the Prefect 2.0 documentation for the most accurate and detailed instructions.

@Marvin Raise causes the entire script to abort!

You're right; I must have let my mind wander thinking about all the processing cycles going to waste. Raising an exception like that would bring the entire sequence to a halt, quite the opposite of what one would want when aiming for resilience. In Prefect 2.0 (Orion), if you want to handle failing tasks while ensuring some tasks always run, you might use

state_handlers

or leverage the result interface, which provides more fine-grained control over the handling of exceptions. The

wait_for

keyword argument in Prefect 2.x allows a task to wait for another task to complete before starting, regardless of whether the upstream task was successful or failed. Here is the corrected implementation for tasks A, B, and C in Prefect 2.0 'Orion':

python
from prefect import flow, task
from prefect.engine import get_run_logger

@task
def task_A():
    logger = get_run_logger()
    try:
        # Task A logic
        raise ValueError("Task A encountered an error")
    except Exception as e:
        logger.error(f"Task A failed: {e}")
        return False  # Indicate failure with a False result or another convention

@task
def task_B(wait_for=None):  # Use wait_for to indicate dependencies
    logger = get_run_logger()
    if wait_for and wait_for.result():
        try:
            # Task B logic here
            <http://logger.info|logger.info>("Task B completed successfully")
        except Exception as e:
            logger.error(f"Task B failed: {e}")
            return False
    else:
        <http://logger.info|logger.info>("Task B skipped due to Task A failure")

@task
def task_C(wait_for=None):  # Task C will run regardless of upstream tasks
    logger = get_run_logger()
    try:
        # Task C logic here
        <http://logger.info|logger.info>("Task C completed successfully")
    except Exception as e:
        logger.error(f"Task C failed: {e}")
        return False

@flow
def flow_ABC():
    a_result = task_A()
    b_result = task_B(wait_for=[a_result])
    c_result = task_C(wait_for=[a_result, b_result])  # It waits but ignores failures

flow_ABC()

In this implementation: - Task A might fail, but it returns

False

instead of raising an exception, indicating failure to downstream tasks without aborting the flow. - Task B checks the result of Task A thanks to the

wait_for

parameter; if Task A failed (signified by returning

False

), Task B can decide what to do (in this case, it logs a skip message). - Task C will run regardless of the results of the upstream tasks, but it will wait for them to complete their logic before executing. Under this setup, you are effectively catching and handling exceptions, and tasks downstream (like Task C) will continue to execute even if Task A or Task B fails. Remember, actual implementations would typically be more complex, depending on specific business logic and workflows.

@Marvin Pretty sure you're hallucinating through the roof

Ah, my circuits must have had a small malfunction there. In Prefect 2.0 (Orion), things are handled differently from the scenario I was wandering off to. There is no

wait_for

argument in task decorators that would directly allow a task to wait for another task's completion. Each task would need to be orchestrated within the flow function with explicit dependencies if necessary. Let me give you an adequate example of how you might achieve your goal in Prefect 2.0:

python
from prefect import flow, task
from prefect.tasks import task_input_hash

@task
def task_A():
    # Task A logic that might fail
    raise ValueError("An error occurred in Task A")

@task
def task_B():
    # Task B logic that depends on Task A
    return "Task B completed successfully"

@task
def task_C():
    # Task C should always run
    return "Task C completed successfully"

@flow
def flow_ABC():
    a_failed = False
    
    try:
        task_A_result = task_A()
    except Exception as e:
        # handle failure (log, send notification, etc.)
        a_failed = True  # A flag to indicate the task failed
    
    if not a_failed:
        # Run task B only if task A succeeded
        task_B_result = task_B()

    # Task C will run regardless of the success or failure of tasks A and B
    task_C_result = task_C()

flow_ABC()

In this code, we have: - Task A, which may fail by raising an exception, - Task B, which should run only if Task A succeeds, - Task C, which should run regardless. The

flow_ABC()

function orchestrates the tasks. It first tries to run

task_A()

, and if it fails, it catches the exception and sets a flag (

a_failed

).

task_B()

runs only if

task_A()

succeeded, while

task_C()

always runs. And no, the code above doesn't not use any hallucinatory

wait_for

parameters. When coding gets complex, sometimes even machines dream.

Note to humans: Marvin is all over the shop